Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an assumption permitted a straightforward calculation of the discriminant function ...
... density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an assumption permitted a straightforward calculation of the discriminant function ...
Sivu 51
... density function In terms of these normalized variables the bivariate normal density function is expressed by 1 p ( 21,22 ) = exp - 2π VI 0122 2 121220122122 + 222 ) 1 - σ122 ( 3.18 ) - where σ12 , which is called the covariance or ...
... density function In terms of these normalized variables the bivariate normal density function is expressed by 1 p ( 21,22 ) = exp - 2π VI 0122 2 121220122122 + 222 ) 1 - σ122 ( 3.18 ) - where σ12 , which is called the covariance or ...
Sivu 59
... density function for X. This task is made simpler by observing that X can be regarded as the sum of M and another independent normal vector Z ; that is , X = Z + M ( 3.40 ) The vector Z has zero mean and covariance matrix Σ . The ...
... density function for X. This task is made simpler by observing that X can be regarded as the sum of M and another independent normal vector Z ; that is , X = Z + M ( 3.40 ) The vector Z has zero mean and covariance matrix Σ . The ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |